When providing healthcare to patients it is frequently important to accurately monitor at least one type of patient parameter associated with the patient. To accomplish this, at least one sensor is connected to a patient for use in sensing physiological signals that are provided to and interpreted by at least one type of patient monitoring device. The sensed physiological signals are used in determining the at least one patient parameter. Sensed signals having poor quality negatively impact the ability of the patient monitoring device to determine the desired patient parameter resulting in potentially inaccurate patient parameter data values. Inaccurate patient parameter data may, at best, reduce the efficiency and competency of the healthcare being provided to the patient and, at worst, may result in harm to the patient. Thus, a need exists to provide a system and method for measuring the quality of a physiological signal that is used in determining and monitoring at least one patient parameter.
An example of a sensed physiological signal is electrocardiogram (ECG) signals that represent a series of heartbeats. ECG data of poor quality presents challenges to accurate interpretation in patient monitoring. Recently, it has become commonplace to use multiple ECG leads as inputs to a multi-lead algorithm for detecting arrhythmia in real-time. The key component in the multi-lead algorithm is determining which ECG leads connected to the patient should be included as inputs and subsequently processed by the ECG monitor. By using leads with inferior quality, the performance of the algorithm will be degraded resulting in inaccurate patient parameter data. Therefore, it is desirable to develop a method of measuring the quality of the ECG signals derived from respective leads connected to the patient to identify and select which leads to be used in determining ECG data for the patient.
Clinical experience with current ECG-based monitoring has shown that the best performance can be achieved if the input signal derived from the patient connected sensors is free from noise as noise has been the primary source of performance degradation for multi-lead algorithms as described above. Noise appearing on the ECG may be due to physiologic or non-physiologic sources. The most common noise may be caused by skeletal muscle tremor, electrical interference and electrode movements. Failure to minimize and recognize artifacts in the input signal that are caused by noise during monitoring may result in incorrect detection of heart rate and arrhythmias leading to false alarms and unnecessary clinician intervention.
Invariably, estimation of noise presence in an ECG input signal will result in the ECG algorithm rejecting part or all of the ECG signal. Alternatively, noise estimation may result in allowing the sensed data to proceed for further analysis taking into account the magnitude of the noise present in the ECG. There are a few techniques detecting individual types of noise (mostly, only for high frequency noise, baseline wander, and low frequency noise). Current methods for the detection and/or quantification of composite noise in ECG signals require the accurate QRS detection for individual leads or one combined lead. For example, the classic method to quantify signal quality of each lead, is to represent ECG signal (QRS, P, T) morphology on a template (aligned averaged signal) or model (KLT functions, wavelets, etc), and define the difference between the signal and the representation as the underlying noise in the ECG. There are also some variations in noise definition which propose using a noise index of the T-P interval average power divided by the QRS average power.
However, a drawback associated with the current ECG processing is that reliable QRS detection is not always achievable if the ECG signal is poor due to the inability to distinguish between desired signal data and noise. It is therefore desirable to measure ECG signal quality and determine weighting factors for use in selecting different leads from a set of leads in order to accurately perform QRS detection for a particular patient. A system according to invention principles addresses deficiencies of known systems.